STATISTICAL ANALYSIS OF INTRACLASS CORRELATIONS FROM MULTIPLE SAMPLES WITH APPLICATIONS TO ARTERIAL BLOOD PRESSURE DATA

Abstract

We consider inference procedures on intraclass correlations for unbalanced data from several multivariate normal populations. We derive several tests, including ones based on Fisher's variance stabilizing transformation and Neyman's score functions, to test the homogeneity of intraclass correlations. We illustrate the methodology with an example that uses arterial blood pressure data collected by Miall and Oldham and we compare the procedures in terms of their empirical levels and powers with a Monte Carlo simulation study. We recommend the use of Neyman's C(alpha) test and a test based on the ANOVA estimators of the intraclass correlations as they hold their significance levels and give consistently higher powers.

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